Undergraduate Module Descriptor

SSIM906: Quantitative Dissertation

This module descriptor refers to the 2021/2 academic year.

Module Aims

To enable you to write an extended piece of independent writing, around a topic of your own choosing using some of the quantitative data-analytic tools you became acquainted with during the programme (e.g., methods for causal inference, Bayesian econometrics, network analysis, text-mining and analysis techniques), in communication with key experts in your chosen area. It will allow you to demonstrate depth and breadth of knowledge in a particular subject area of professional or intellectual interest. The dissertation will be a mark of your ability to express yourself in writing.

Intended Learning Outcomes (ILOs)

This module's assessment will evaluate your achievement of the ILOs listed here – you will see reference to these ILO numbers in the details of the assessment for this module.
On successfully completing the programme you will be able to:
Module-Specific Skills1. Demonstrate knowledge in depth of a specialised subject area (may include a specific statistical technique)
2. Design an individual research programme, incorporating appropriate quantitative social science research methods
3. Collate and analyse primary or secondary data related to a subject discipline from appropriate sources.
Discipline-Specific Skills4. Assimilate and critically analyse data from an appropriate range of sources, from primary or secondary data sets
5. Develop cogent argument and apply appropriate statistical techniques
6. Communicate complex information and ideas effectively in writing.
Personal and Key Skills7. Use IT for information retrieval and presentation.
8. Manage own work

Indicative Reading List

This reading list is indicative - i.e. it provides an idea of texts that may be useful to you on this module, but it is not considered to be a confirmed or compulsory reading list for this module.

G King, R Keohane and S Verba, Designing Social Inquiry (Princeton UP, 1994);

D Burton (ed), Research Training for Social Scientists: A Handbook for Postgraduate Researchers (Sage, 2000).

S. Jackman. Bayesian Analysis for the Social Sciences (Wiley, 2000).

W. Greene. Econometric Analysis (Pearson, 2012).

Angrist, J., and Pishke, S. Mostly Harmless Econometrics (Princeton University Press, 2009).

Gelman, Andrew, and Jennifer Hill. Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, 2006.

Kosuke, I.. Quantitative Social Science: An Introduction. (Princeton University Press, 2017)

Wooldridge, J. Econometric Analysis of Cross-Section and Panel Data (2010, MIT University Press).

Subject-specific reading will varying according to research topic.